brainpy.dyn.layers.Dense#

class brainpy.dyn.layers.Dense(num_in, num_out, W_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=- 2, out_axis=- 1, distribution=truncated_normal, seed=4357737), b_initializer=ZeroInit, mode=TrainingMode, name=None)[source]#

A linear transformation applied over the last dimension of the input.

Mathematically, this node can be defined as:

\[y = x \cdot W + b\]
Parameters
  • num_in (int) – The number of the input feature. A positive integer.

  • num_out (int) – The number of the output features. A positive integer.

  • W_initializer (optional, Initializer) – The weight initialization.

  • b_initializer (optional, Initializer) – The bias initialization.

  • mode (Mode) – Enable training this node or not. (default True)

__init__(num_in, num_out, W_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=- 2, out_axis=- 1, distribution=truncated_normal, seed=4357737), b_initializer=ZeroInit, mode=TrainingMode, name=None)[source]#

Methods

__init__(num_in, num_out[, W_initializer, ...])

clear_input()

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

offline_fit(target, fit_record)

The offline training interface for the Dense node.

offline_init()

online_fit(target, fit_record)

online_init()

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

reset([batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state([batch_size])

Reset function which reset the states in the model.

save_states(filename[, variables])

Save the model states.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

unique_name([name, type_])

Get the unique name for this object.

update(sha, x)

The function to specify the updating rule.

update_local_delays([nodes])

Update local delay variables.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

global_delay_data

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.